As technologies pertaining to computing have advanced in recent years, an amount of data that can be stored and analyzed has increased exponentially. For example, personal computers are currently equipped with hard drives that have storage capacity on the order of multiple terabytes. Thus, an individual utilizing a personal computer can store millions of documents, spreadsheets, images, amongst other types of data.
When a large amount of related data is accumulated, determining how portions of the related data correspond to other portions of the related data can become quite difficult. Visualization mechanisms have been adapted to present an individual or set of individuals with a visual depiction of how portions of data relate to each other. In an example, a mechanism referred to as a treemap has been used to present a visual depiction of a hierarchical arrangement of data to an individual or set of individuals.
Historically, treemaps are a mechanism to graphically depict tree-structured (hierarchical) data utilizing nested rectangles. Specifically, each branch of a tree is given a rectangle that is proportional in size to a dimension of the underlying data. A rectangle may be tiled with additional rectangles to illustrate sub-branches of a particular branch. Thus, by reviewing a treemap, an individual or set of individuals can easily ascertain how a set of data is structured and can infer relationships amongst the data.
While treemaps are useful in visually depicting tree-structured data, they are generally viewed as being somewhat inflexible. For example, altering a size of a rectangle that represents the source node of tree-structured data may cause a resulting treemap to become visually unappealing (e.g., rectangles representing branches of the data may be resized as very thin rectangles that provide little to no meaning to a reviewer of the treemap). Accordingly, a visualization mechanism entitled a Voronoi treemap has been utilized to overcome such deficiency.
Voronoi treemaps have similar characteristics to treemaps in that Voronoi treemaps can represent tree-structured data, wherein size of subareas in a Voronoi treemap is proportional to a dimension of underlying tree-structured data. Voronoi treemaps, however, lack constraints that are associated with conventional treemaps. Specifically, portions of Voronoi treemaps may be or include any suitable shape (while maintaining a desired aspect ratio between height and width of shapes), and need not be rectangular in nature. While Voronoi treemaps allow areas to be resized, zoomed in upon, and/or moved, generating a Voronoi treemap requires a significant amount of processing time.